Social Learning with Model Misspecification: A Framework and a Characterization

This paper develops a general framework to study how misinterpreting in-formation impacts learning. We consider sequential social learning and passive individual learning settings in which individuals observe signals and the actions of predecessors. Individuals have incorrect, or misspecified models of how to in-terpret these sources – such as overreaction to signals or misperception of others’ preferences. Our main result is a simple criterion to characterize long-run beliefs and behavior based on the underlying form of misspecification. This provides a unified way to compare different forms of misspecification that have been previ-ously studied, as well as generates new insights about forms of misspecification that have not been theoretically explored. It allows for a deeper understanding of how misspecification impacts learning, including exploring whether a given form of misspecification is conceptually robust, in that it is not sensitive to parametric specification, whether misspecification has a similar impact in individual and so-cial learning settings, and how model heterogeneity impacts learning. Lastly, it establishes that the correctly specified model is analytically robust, in that nearby misspecified models generate similar long-run beliefs.

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